---
license: other
license_name: nutrient-eval-restricted
pretty_name: Document Classification Benchmark (open-vocab, zero-shot)
task_categories:
- zero-shot-image-classification
- image-classification
language:
- en
tags:
- document-ai
- open-vocabulary
- zero-shot-image-classification
- document-image-classification
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype: string
  - name: candidate_labels
    list: string
  - name: source
    dtype: string
  - name: license_tag
    dtype: string
  - name: doc_id
    dtype: string
  - name: track
    dtype: string
  splits:
  - name: test
    num_bytes: 453775818
    num_examples: 1274
  download_size: 453674933
  dataset_size: 453775818
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# Document Classification Benchmark (open-vocab, zero-shot)

**Given a document image and an arbitrary set of text labels, which one is right?** A held-out, zero-shot,
*open-vocabulary* evaluation for document-type classification — labels are supplied at inference, not baked
into a head. Test split only; not for training. Every image is drawn from a **permissively-licensed,
redistributable** source.

Powers the
[document-classification-leaderboard](https://huggingface.co/spaces/nutrientdocs/document-classification-leaderboard)
and evaluates [`document-classification-v2`](https://huggingface.co/nutrientdocs/document-classification-v2)
(commercial) and the open-weight
[`document-classification-v1`](https://huggingface.co/nutrientdocs/document-classification-v1); try it live in
the [demo](https://huggingface.co/spaces/nutrientdocs/document-classification-demo).

## Tracks (macro-F1)

- **DocLayNet** — document page categories (financial reports, scientific articles, manuals, patents, …).
- **Forms** — form vs non-form and fine form sub-types.
- **OOD** — document *types absent from training* (invoices, handwriting, charts, tables) — the open-vocab
  stress test.
- **OOV** — an evaluation *protocol* (not extra rows): score the same images against never-seen synonym
  paraphrases of each label ("invoice" → "bill") to measure concept-matching vs memorized wording.

## Contents & sources

897 test images (`track` column selects the slice). All sources are redistributable:

| Source | Track | License |
|---|---|---|
| DocLayNet (`ds4sd/DocLayNet`) | doclaynet | CDLA-Permissive-1.0 |
| synthetic IRS forms (public-domain templates + faked fields) | forms | public-domain |
| mixed permissive HF sources (invoice / handwriting / chart / table) | ood | per upstream source |

> **Tobacco3482 is deliberately excluded.** Its research-only license is not redistributable, so it is not
> part of this public benchmark (it is used only in internal evaluation).

```python
from datasets import load_dataset
ds = load_dataset("nutrientdocs/document-classification-benchmark", split="test")
```

## Schema

| field | meaning |
|---|---|
| `image` | the document image (RGB) |
| `label` | gold document-type label |
| `candidate_labels` | the open-vocab candidate set for the row's track |
| `source` | originating dataset |
| `license_tag` | redistribution tier of the source |
| `doc_id` | provenance id |
| `track` | doclaynet · forms · ood |

## About the author

<a href="https://nutrient.io/">
  <img src="https://avatars2.githubusercontent.com/u/1527679?v=3&s=200" height="80" />
</a>

This project is maintained and funded by [Nutrient](https://nutrient.io/) - The deterministic document infrastructure enterprises run their highest-stakes workflows on: replayable output, clear exceptions, and full audit trails on the messy, regulated documents where AI alone breaks.